For businesses across the globe, AI has largely failed to deliver on its hype. In fact, new research from MIT’s NANDA initiative reveals some troubling facts: Just 5% of AI pilot programs have made it into production with measurable value, which means that 95% have failed to deliver tangible impact on the bottom line. With a large number of observers now questioning the limitations of generative AI, many leaders are now wondering whether it is possible to capture real ROI using the technology.
Businesses have long history of chasing the “next big thing.” While AI stands apart from other much-hyped technologies (blockchain, the metaverse), it can be easy to see it as the most recent development in a long line of projects that are hard to explain or justify. At Shibumi, we’ve witnessed the successful implementation of AI initiatives in enterprises across industries and borders. We know there is power in it—but how can more businesses harness that power?
Achieving ROI through AI initiatives is possible—but it requires alignment, strategic planning, and robust data analysis.
Enterprise-wide alignment matters more than algorithms
Siloed data, projects, and initiatives. Strategies that live in PowerPoint presentations instead of in day-to-day operations—these are common pain points that technology can’t solve. In fact, when more technology—like a new AI initiative—is added ot the tech stack, misalignments tend to be amplified. After all, automating flawed processes only means suboptimal processes are completed more quickly.
MIT’s research underlines this common outcome for businesses angling to integrate AI into their tech stack, pointing out that most enterprise tools fail not because of the technology itself, but because the organizations themselves are not equipped to use it successfully. Many enterprises opt for tools that don’t adapt to the business use case, can’t integrate feedback, and don’t fit into daily workflows.
The solution? Businesses must understand, define, and share the real business problem that AI can address. Solving for it must become a business priority, and that objective must be communicated and integrated into operations at every level through enterprise alignment processes that fill the gap between strategy and execution.
Strategic Planning: Keeping Transformation on Track
Effective transformations aren’t just about launching new technologies, but embedding those investments into business in a comprehensive and meaningful way. Businesses must find ways to create alignment and visibility across programs, investments, and initiatives in order to link AI programs to strategic goals.
With clarity into how and where every initiative is having impact, business leaders can make decisions not just about whether they need to leverage AI, but about how it can be applied. Additionally, every AI investment’s ROI should be tracked throughout the program’s entire life cycle. With full-cycle strategic planning it quickly becomes clear whether AI is aligned with strategic organizational goals, a crucial factor in determining the program’s success.
Today, there are many solutions on the market that facilitate strategic planning with dashboards that track key metrics across the initiative lifecycle and produce executive reports that enable leaders to understand precisely where value is being generated—or not. These tools can accelerate meaningful integration of AI into operations and prevent wasteage associated with failure to link IT assets to operations.
Measuring Impact with Robust Data Analytics
Many businesses fail to sufficiently measure the outcomes AI programs produce in their business, which can hamstring efforts to optimize. Alternatively, data quality may be lacking, which can produce inaccuracies and obscure actual ROI.
In order to successfully use AI, businesses must not just measure the obvious KPIs, but also track indirect returns—things like accelerated decision-making, improved customer experience, reduced risk exposure, and greater organizational agility. Metrics like these tracked in tandem with monetary outcomes can create a comprehensive picture of ROI. Simultaneously, enterprises must employ platforms that ensure data is governed appropriately and doesn’t become siloed.
With access to robust, accurate data leaders can connect outcomes back to strategic objectives. An AI model that increases efficiency in one isolated department, for example, might not be worth the investment when looked at through the lens of enterprise-wide priorities. This is just one example of how, with strong analytics frameworks in place, leaders can compare initiatives side-by-side, allocate resources with confidence, and adjust priorities and investments as necessary.
The Tools to Turn AI From Hype to Enterprise Value
AI’s promise is undeniable—and the businesses that will benefit most from it most are clued in to the importance of how they organize, plan, and measure their AI transformations. Enterprises that treat AI projects as a bolt-on experiments will inevitably receive mixed results, likely falling into the 95% percent margin of failed initiatives. But those that embed AI into their strategy, connect it to enterprise priorities, and rigorously track ROI will be able to unlock sustainable value.
When AI initiatives are anchored in business priorities, they transform the technology from over-hyped to a true enabler of resilience and competitive advantage. Find out solutions like Shibumi support enterprises on their journey to capture AI’s ROI and continue to evolve alongside this—and every—wave of innovation.